Genome

Species -level identification of the blowfly Chrysomya megacephala and other Diptera in China by DNA barcoding

Journal: Genome

Manuscript ID gen-2015-0174.R2

Manuscript Type: Article

Date Submitted by the Author: 12-Jul-2016

Complete List of Authors: Qiu, Deyi ; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center Cook, Charles ; European Molecular Biology Laboratory, European BioinformaticsDraft Institute (EMBL-EBI), Wellcome Genome Campus YUE, Qiaoyun; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center, Hu, Jia; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center Wei, Xiaoya ; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center Chen, Jian; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center Liu, Dexing; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center Wu, Keliang; Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center

Keyword: blowfly, haplotype network, invasive species, Diptera, pest

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Species-level identification of the blowfly Chrysomya megacephala and other Diptera in China by

DNA barcoding

Deyi Qiu 1, Charles E. Cook 2, Qiaoyun Yue 1, *, Jia Hu1, Xiaoya Wei 1, Jian Chen 1, Dexing Liu 1, and

Keliang Wu 1

1. Zhongshan Entry-Exit Inspection and Quarantine Bureau Technology Center, 2, Zhongshan 6

road, Zhongshan 528403, Guangdong, China

2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),

Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK * Corresponding author: [email protected],Draft [email protected]

Conceived and designed the experiments: QY, DQ. Performed the experiments: QY, DQ, JH, XW,

JC, DL, KW. Analyzed the data: QY, DQ, CEC. Wrote the paper: QY, CEC.

Competing Interests: The authors have declared that no competing interests exist.

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Abstract

The blowfly Chrysomya megacephala , or oriental latrine fly, is the most common human-associated fly of the oriental and Australasian regions. C. megacephala is of particular interest for its use in forensic entomology and because it is a disease vector. The larvae are economically important as feed for livestock and in traditional Chinese medicine. Identification of adults is straightforward, but larvae and fragments of adults are difficult to identify. We collected

C. megacephala , its allies Chrysomya pinguis and Protophormia terraenovae, as well as flies from

11 other species from 52 locations around China, then sequenced 658 base pairs of the COI barcode region from 645 flies of all 14 species, including 208 C. megacephala , as the basis of a

COI barcode library for flies in China. While C. megacephala and its closest relative C. pinguis are closely related (mean K2P divergence of 0.022), these species are completely non-overlapping in their barcode divergences, thus demonstrating the utility of the COI barcode region for the identification of C. megacephala . WeDraft combined the 208 C. megacephala sequences from China with 98 others from public databases and show that worldwide COI barcode diversity is low, with

70% of all individuals belonging to one of three haplotypes that differ by one or two substitutions from each other, reflecting recent anthropogenic dispersal from its native range in Eurasia.

Keywords

Haplotype network, blowfly, invasive species, Diptera, pest

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Introduction

The blowfly Chrysomya megacephala (Fabricius), or oriental latrine fly, is the most common

human-associated fly of the oriental and Australasian regions (Wall and Shearer 1997). C.

megacephala larvae develop in feces and decomposing flesh and consequently can be found at

extremely high density (>95% of flies) under some environmental circumstances, such as

locations near fish-processing activities (Wall et al. 2001). C. megacephala is native to Eurasia but

through human action has spread around the world: by December 1975 it was reported from South

America (Brazil) (Imbiriba et al. 1977) and later became established in New Zealand, Africa

(Williams and Villet 2006), and then in North, South, and Central America via harbours and

airports (Wells 1991; Williams and Villet 2006). It has a reported distribution across the whole of

China except for arid high-elevation regions in Xinjiang, Qinghai, and Tibet (Xue and Zhao 1996).

C. megacephala is of particular importance to humans for a range of reasons: 1) it is

considered as one of the most importantDraft fly species in the science of forensic entomology (Cai et

al. 2005; Goff 2001; Shi et al. 2008; Wu and Hu 2012; Xue and Zhao 1996); 2) in traditional

Chinese medicine wuguchong, the dried larva of C. megacephala is believed to have the curative

effect of clearing stagnant heat-toxicity from the human body (Luo 1993); 3) live larvae are used

in medicine in the form of “maggot therapy” (Taha et al. 2010); 4) it is an important source of

animal feed protein (Sing et al. 2012); 5) it can cause myiasis (or fly strike) in sheep and

occasionally in humans as it can invade open wounds (Bunchu et al. 2007); and 6) C.

megacephala is also a disease vector and is known to lay eggs on human feces and subsequently

transmit diseases such as bacterial gastroenteritis if it comes into contact with human food

(SukontasonDNA barcoding et al. 2007). has been successfully used for the molecular identification of a broad variety

of insect taxa, including many Diptera (Nelson et al. 2007; Hernandez-Triana. 2015; Liao et al.

Renaud et al. 2012; Rivera and Currie 2009; Schuehli et al. 2007), including C. megacephala and

the closely related species C. pinguis (Nelson et al. 2012; Ramaraj et al. 2014; Salem et al. 2015)( .

DNA barcoding, usually of a specific region in the mitochondrial cytochrome c oxidase subunit I

(COI) gene, generally relies on the observation that intraspecific COI variation is usually lower

interspecific variation (Raupach et al. 2014). Consequently, comparative sequence analyses

typically, but not always, reveal a “barcoding gap” (Meyer and Paulay 2005) on plots of pairwise

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sequence differences and thereby allow molecular species-level identification of sequences generated from unidentified or unidentifiable samples, such as insect larvae or bloodstains (Hebert et al. 2003, 2004).

DNA barcoding has been criticized as a single-character typological approach that cannot replace systematic science and will not work for all clades (DeSalle et al. 2005; Ebach 2011;

Klausnitzer 2010; Will et al. 2005). Nevertheless, it has become an important, useful, and increasingly used tool for species descriptions (Butcher et al. 2012; Hendrich and Balke 2011;

Stoev et al. 2010; Tamura et al. 2013; Wesener 2012; Wesener et al. 2011) as well as various other biological disciplines Adamowicz 2015), including forensics (Ferri et al. 2009; Meiklejohn et al.

2011), pest biology (Engstrand et al. 2010), Inspection and Quarantine (Liao et al. 2014; Liu et al.

2014; Wei et al. 2014; Yue et al. 2013), and conservation biology (Neigel et al. 2007; Ward et al.

2008). Examples are the recommendation of barcoding for identification of flightless weevils in the genus Trigonopterus as a substituteDraft for a traditional morphological key (Riedel et al. 2013); identifying the sources of food substitution or contamination (Cawthorn et al 2012 ); identifying the presence of genetically modified organisms (Barcaccia et al 2016 ); and identifying birds

“minced” in jet engineers (Wong and Hanner 2008; Grant 2007). In sum, DNA barcoding has proven both useful and reliable for species identification, particularly for degraded or partial specimens, for many taxonomic groups. This identification is only possible, though, if data from reliably identified specimens are available in public databases.

Adult C. megacephala are easily recognizable by experts, but less so for non-experts, while eggs, larvae, and fragments of adults, all of which may be encountered by pest control or public health workers, cannot be identified morphologically. A related question is whether C. exhibits any geographic structure that might allow assignment of place of origin to a sample of unknown provenance. Despite the ubiquity and economic importance of this species, C. megacephala barcode sequences, like those of many arthropods, are still poorly represented in public databases. In this study we collected C. megacephala as well as flies from 13 other species seven other genera, from 52 localities around China in order to confirm the utility of DNA for identification of the economically important C. megacephala and to establish a basic barcode library for C. megacephala and other related flies that co-occur with this species. There are 39 named species of the genus Chrysomya (http://eol.org/pages/56219/overview accessed 23 June gen-2015-0174.R2 4 https://mc06.manuscriptcentral.com/genome-pubs Page 5 of 37 Genome

2016), of which three are common in China: C. megacephala, C. pinguis, and C. phaonis . C.

commonly co-occurs with C. megacephala and is the closest relative of C. megacephala (Yang, et

2014). We successfully collected individuals of C. pinguis and report the sequences here. We did

identify any C. phaonis specimens from our sampling and therefore cannot yet report C. phaonis

barcodes, but adults of C. phaonis are morphologically distinct from C. megacephala (Yang, et al

2014) and it is very unlikely that C. phaonis samples would be confused for C. megacephala . We

will report C. phaonis barcode sequences if samples become available in future. We also compared

C. megacephala COI barcode sequences from our work with other publicly available C.

megacephala sequences to assess whether variation within China is comparable to worldwide

variation in this species.

Material and Methods Sample collection Draft Adult flies were collected with a sweep net from 52 different localities in China during the

summers of 2012 and 2013. As this was not an ecological study, we did not undertake random

transects. Instead, to collect as many specimens as possible, we walked continually for up to two

hours for a distance of roughly one kilometer, sweeping the nets frequently but also specifically

targeting any flies we saw. Adults of all species collected were provisionally identified by

morphology （Xue and Zhao 1996 ；Fan 1992 ）. We also processed three individuals of Musca

domestica that were intercepted in waste paper from California to Zhongshan (Guangdong China),

three C. megacephala intercepted in waste paper from Manila, and six C. megacephala intercepted

in waste paper from Lima. Specific permission was not required for collecting in these localities,

and none of the species collected are endangered or protected. Identified specimens were verified

and accessioned in the insect collection of Zhongshan Entry-Exit Inspection and Quarantine

Bureau.

DNA barcoding

Genomic DNA extraction and PCR amplification

When available, we selected three specimens of each species at each collection locality for

molecular analysis. A hind leg was removed from each specimen and placed in a 1.5 ml Eppendorf

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tube with 95% ethanol. All instruments used to remove leg tissues were cleaned with 70% ethanol and flame sterilized between manipulation of each specimen. DNA was extracted from tissue following the standard protocols of the TIANamp Genomic DNA Kit (DP304, TIANGEN). The barcode region of COI was amplified using primer pair of LCO1490 and HCO2198 (Folmer et al.

1994).

Polymerase chain reactions were conducted in a 50 µl volume: 10× Taq polymerase buffer 5

µl, dNTP (2.5 mM each) 2 µl, primer (20 µM) 1 µl each, Taq polymerase (5 U/µl) 0.5 µl, DNA template 100 ng, add ddH 2O up to 50 µl. All PCR reagents were from TIANGEN (Beijing).

Reaction conditions were 95 ℃ 3 min; 95℃ 45 s, 50 ℃ 45 s, 72 ℃1 min, 34 cycles; 72 ℃ 10

PCRmin. product purification and sequencing

PCR products were purified and cloned as previously described: three colonies from each cloned PCR product were sequenced from both ends, and a consensus sequence from each clone was used for all analyses (Yue et Draft al. 2014). Sequencing was successful for all individuals attempted.

Additional sequences

In addition to the sequences we generated from flies caught for this study, we also searched the

Barcode of Life Data Systems (BOLD, Ratnasingham and Hebert 2007) public data portal for

Chrysomya megacephala COI sequences and identified 98 sequences that included the barcode region, as generated for this study. The BOLD portal includes all current C. megacephala sequences in GenBank. BOLD accession numbers for these sequences are listed in supplemental information (Table S1). Only eight of these 98 sequences included latitude and longitude coordinates of the collection site. Eight of the sequences were incomplete at the 5’ end: three were missing three bases and five were missing 13 bases. These were encoded as missing. All listed country of collection, with 37 listing a town, city, or other local place name. Since the majority of these sequences lacked precise geographic coordinates we used only country of collection to produceData analysis network diagrams.

After removing primers, all sequences were 658 base pairs (bp) long and contained no deletions, or stop codons, and were translatable into the expected 219 residues of the

COI gene. We used the MAFFT algorithm (http://www.ebi.ac.uk/Tools/msa/mafft/) to confirm the alignment. gen-2015-0174.R2 6 https://mc06.manuscriptcentral.com/genome-pubs Page 7 of 37 Genome

Three separate datasets were extracted for intraspecific and interspecific analyses. For

intraspecific analysis, we assembled one dataset with the 208 C. megacephala sequences,

comprising 37 distinct haplotypes, from flies collected for this study and another of 306 C.

megacephala sequences: the 208 from China plus the 98 downloaded from BOLD. This second

dataset contained 53 unique haplotypes. The 208 “Chinese” flies included the three individuals

from Manila and the six individuals from Lima that were intercepted at the port of Zhongshan, as

these were collected in China. For interspecific analysis we assembled one dataset with sequences

of 208 C. megacephala , 36 C. pinguis , and 13 P. terraenovae collected in China. For the

intraspecific datasets, distances for each sequence pair were calculated as described below,

assigned to a range interval, and the number of pairwise distances within each interval tallied and

charted. For the two larger C. megacephala -only datasets, we also created network diagrams in

order to examine geographic variation between C. megacephala . For the worldwide data set of 306 C. megacephala we assigned the nineDraft individuals intercepted at the port of Zhongshan to their countries of origin (the Philippines and Peru) as representatives of haplotypes in those countries.

Mega version 6.06 was used for additional data analysis (Tamura et al. 2013). Mean

frequencies (%) of each nucleotide and nucleotide pair (A+T and G+C) were calculated in MEGA

to evaluate whether nucleotide frequencies were comparable to those typical of insects in general

for this COI gene region (Renaud et al. 2012). We generated Kimura two-parameter (K2P)

distances using the default parameters (transitions + transversions, gamma distribution) for the

entire data set. Pairwise distance calculations in Mega ignore missing bases so some pairwise

distances used slightly shorter total sequence lengths to calculate pairwise distances. We tested

other distance models and note that our results were extremely robust and not sensitive to changes

in the model used.

We used Microsoft Excel to tally the number of distance pairs in selected range intervals for a

data set with C. megacephala and the two sympatric species whose barcode regions proved most

similar to it: C. pinguis and Protophormia terraenovae . Our method allows changing the size of

each interval, and again our results were robust over different interval ranges, merely changing the

number of columns in each output chart. Network diagrams were constructed for C. megacephala

sequences with PopART (Leigh and Bryant 2015).

To further explore the relationships between C. megacephala, C. pinguis, and P. terraenovae

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we also undertook a phylogenetic analysis using a dataset in which each haplotype was as a single sequence. This dataset included 53 unique C. megacephala , 11 C. pinguis , and 8 P. terraenovae haplotypes, with a single Achoetandrus rufifacies sequence used as the outgroup. A maximum likelihood analysis was performed using Mega 6.06 using the T92+G model, which model testing within Mega 6.06 identified as the best model for these data using the criterion of lowest Bayesian information criterion (BIC) score. Additionally, we used Mega 6.06 to generate

1000 neighbor-joining bootstrap replicate distance trees, using maximum likelihood distance matrices generated using the same T92+G model.

Results

We sequenced the COI barcode region of 645 fly specimens from 14 species in three families, including two Chrysomya species ( C. megacephala, C. pinguis ) and six other calliphorids. The mean nucleotide content of the COIDraft sequences was A (30.0%), T (38.4%), G (15.7%), and C (15.9%). A + T (68.4%) was in higher proportion than G + C (31.6%) and was comparable to those typical of insects in general for this COI gene region and for other dipteran mitochondrial sequences (Renaud et al. 2012; Rivera and Currie 2009; Schuehli et al. 2007). Collection locations are mapped in Fig. 1.

The focus of this project was barcoding C. megacephala and its close relatives; hence, 208 of

645 sequenced flies were C. megacephala , and 83 others were C. pinguis , Achoetandrus rufifacies , and Protophormia terraenovae , all from the Calliphoridae. C. pinguis is believed to be the closest relative of C. megacephala in China (Yang et al. 2014), and the evidence from the work reported here supports this conclusion, but we note that COI barcode sequences are not yet available for the less common C. phaonis. . A. rufifacies, C. megacephala , and C. pinguis are all classified within the subfamily Chrysomyinae, while P. terraenovae is classified in a separate subfamily, the

Phormiinae, but mean K2P distances between C. megacephala and A. rufifacies were 0.07, with a range of 0.065 to 0.08, about 15% greater than the distances between C. megacephala and P. terraenovae. Therefore, our subsequent distance analyses use P. terraenovae as a third species rather than A. rufifacies .

Sequences from the other 11 species are reported here and have been submitted to GenBank, are not otherwise analyzed due to relatively small numbers of sequences and to their taxonomic gen-2015-0174.R2 8 https://mc06.manuscriptcentral.com/genome-pubs Page 9 of 37 Genome

distance from C. megacephala , which is the focus of this paper. Table 1 lists all species and the

number of individuals sequenced. Collection information and GenBank accession numbers for all

sequenced specimens are summarized in supplemental Table S2.

Interspecific variation in Chrysomya megacephala, Chrysomya pinguis, and Protophormia

terraenovae in China

A primary goal of this work was to determine whether fly larvae and other difficult-to-identify

samples—such as fragments of adult bodies—can be identified using mitochondrial COI barcode

sequences. We tested the practical utility of barcoding for identifying specimens to the species

level using pairwise comparisons between all individuals of C. megacephala and each of the two

other sympatric species with the most similar COI barcoding regions: C. pinguis and P.

terraenovae (Fig. 2). These results show a mean intraspecific Kimura two-parameter (K2P)

distance between individuals of C. megacephala of 0.0028 and mean interspecific K2P distance between individuals of C. megacephalaDraft and C. pinguis of 0.022. Significantly, there is no overlap between the intra- and inter- specific distributions, with a minimum interspecific K2P distance of

0.016 between any two individuals of different species and a maximum intraspecific K2P distance

of 0.011 among individuals of C. megacephala . Our analysis of interspecific differences is of

course based on a modest number of sequenced individuals: 13 P. terraenovae , 36 C. pinguis , and

208 C. megacephala and should be confirmed by collection and sequencing of additional

individuals. Nevertheless, the results are strong enough that we are confident the COI barcoding

region is useful for differentiating biological material between the two Chrysomya congeners in

China. A phylogenetic analysis (Figure S1) separates C. megacephala , C. pinguis , and P.

terraenovae into three distinct clades with 99 percent bootstrap support, further confirming the

utility of the COI barcode region for distinguishing these three species. Barcode sequences from

the other 11 species that we collected are significantly different, and readily differentiated, from

those of C. megacephala and are not further discussed.

Intraspecific variation in Chrysomya megacephala

The analysis above demonstrated that COI barcode sequences differentiate C. megacephala

from its close relative in China. To understand whether C. megacephala exhibits any geographic

structure that might allow assignment of place of origin to a sample of unknown provenance, we

assembled and analyzed two datasets, one with the 208 C. megacephala sequences including 37

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haplotypes from individuals captured for this study and a second expanded dataset with those 208 sequences as well as 98 more from the BOLD database to examine this question.

In the network diagram six of the haplotypes were represented by 5 or more specimens, with one including 97 specimens—almost half of the entire dataset, and two others with 37 and 11 identical specimens (Fig. 3a). These three haplotypes, differing by only one or two base pairs, include 70% of the entire dataset. Five haplotypes were represented by three individuals, one was observed from two individuals, and 24 were singletons. The network diagram shows that most of the sequences are just a few mutation steps away from one of the three large haplotype groups.

There is some possible geographic structure, as individuals from Hainan and the southwest

(Sichuan and Yunnan) do not appear in the 37-member haplogroup or its near neighbours.

However, there are no diagnostic haplotypes that would clearly identify an individual as belonging to a certain geographic area, and the sample sizes from Hainan (11 individuals) and the southwest (9 individuals) are too low to confirmDraft this observation for these regions. We cannot conclude at present that COI barcodes are useful for identifying the geographic origin of C. megacephala within China.

Fig. 3b combines the 208 sequences from flies sequenced for this study with 98 publicly available sequences from BOLD that originated in nine different countries. The pattern of diversity in this network is identical to the pattern shown in Fig. 3a, with the same three large closely related haplotype groups. The maximum path length through the network is 13 steps. Flies from Malaysia and Egypt are represented in the two largest haplotype groups, whereas flies from other geographic regions are represented only in the largest group, with the exception of a single individual from Australia on its own branch two steps from the largest haplogroup. As with flies originating in China, there are no clearly identifiable markers for geographic origin in this dataset.

Given the relatively small sample sizes (excepting Malaysia_Singapore) this result is not surprising, particularly since C. megacephala is an introduced species in Egypt, Australia, and the

Americas and may have reduced mitochondrial diversity due to founder effects. Nevertheless, the overall similarity of the networks in Fig. 3a and Fig. 3b suggests that we have in these data captured most of the worldwide diversity within the COI barcode region for C. megacephala.

Discussion gen-2015-0174.R2 10 https://mc06.manuscriptcentral.com/genome-pubs Page 11 of 37 Genome

C. megacephala has been reported throughout China, from the plains of the eastern coastal

areas to the Inner Mongolian plateau and into the hills and mountains bordering the Tibetan

plateau: that is, all provinces except for Xinjiang, Qinghai, and Tibet (Xue and Zhao 1996). Lack

of records from these three provinces may be due to inadequate sampling during periods when this

fly is active, as was found to be true in South Africa (Williams and Villet 2006). Our field

collection confirmed the distribution of C. megacephala throughout eastern China, and four of us

(QY, DL, KW, JC) also spent two weeks in Tibet (28 th of June to 9 th of July of 2013) searching for

flies from Lhasa to Nyingchi (including Nyingchi city, Motuo and Paizhen), with no success (Fig.

1). There are also records for C. megacephala in Inner Mongolia, and we did capture flies in

Hohhot, the capital city of Inner Mongolia, but we did not find any individuals either in Erenhot or

in Manzhouli, at the international borders with Mongolia and Russia. Nor did we find any

individuals in Mohe, Heilongjiang Province, at the most northern international border with Russia. Our results confirm previous work (XueDraft and Zhao 1996) that C. megacephala does not occur in the far western, northwestern, or northern border regions of China. These are regions of extreme

cold, little rainfall, and, in the far west, of high elevation, suggesting that this species cannot

survive low temperatures, arid conditions, or both.

The primary goal of this study was to determine whether C. megacephala , which is an

economically and forensically important fly, can be unambiguously identified in China using the

COI barcoding region. Our results show that both C. megacephala and C. pinguis , its closest

relative in China, are easily distinguished using the COI barcode region, with no overlap between

the intra- and inter- specific distributions (Fig 2).

We were interested in whether the three C. megacephala individuals from Manila and six from

Lima that were intercepted at the port of Zhongshan were identifiably different from other flies in

China, but all nine belonged to the most common COI haplotype. This is a clear indication of the

cosmopolitan distribution and recent anthropogenic dispersal (Imbiriba et al. 1997; Wall et al.

2001; Wells 1991).

The two other calliphorids closest to the two Chrysomya spp. among the species sampled here,

Achoetandrus rufifacies and Protophormia terraenovae , are also easily identified using the

barcoding region, with well over 5% divergence between these and either Chrysomya spp.

Interestingly, A. rufifacies is currently classified in the same subfamily, the Chrysomyinae, as

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Chrysomya spp., but in fact the COI sequences of P. terraenovae , currently assigned in the subfamily Phormiinae, were more similar to those of the two species of Chrysomya than were those of A. rufifacies . Given the small number of individuals of A. rufifacies and P. terraenovae , and the short length of the COI barcoding region, this result is not definitive, but does suggest that additional work on the phylogeny of the Chrysomyinae might be warranted.

Comparison of the network diagram showing C. megacephala collected in China (Fig. 3a) to that showing worldwide C. megacephala (Fig. 3b) suggests that variation in the barcode region we observed within China includes most of the variation seen worldwide for this species, and we hypothesize that additional sequencing for this species will not expand the network significantly.

Again, though, sample sizes and geographic sampling were low outside of China, so additional work is needed to reach a definitive conclusion. Nevertheless, given the very high frequency of three very closely related haplotypes, it is clear that most samples from C. megacephala collected from anywhere in its worldwide rangeDraft should be unambiguously identifiable using the tools available on the Barcode of Life Data Systems portal or even with a standard BLAST search. In fact, our results suggest that a majority of all collected individuals would have one of the three most common sequences.

Fieldwork is time consuming and expensive, and collecting by sweep net is imprecise, so our collections, as described above, included hundreds of individuals from a number of other species.

The number of individuals from each of these other species was too low for the robust analysis that we have presented for C. megacephala , but these samples are, nevertheless, important additions to the corpus of publicly available barcodes from Chinese insects. Barcodes were generated from a single leg of each individual and released publicly (Table S2); the rest of each fly has been deposited as voucher specimens into the collection of the Zhongshan Entry-Exit

Inspection and Quarantine Bureau, where they are available for additional study. Such collections play a vital role in providing information on the location and spread of living organisms, and, like sequence databases, become more useful as more samples are added, regardless of how familiar or common the species might appear to be (Williams and Villet 2006).

Acknowledgments

This work was financially supported by National Science and Technology support program gen-2015-0174.R2 12 https://mc06.manuscriptcentral.com/genome-pubs Page 13 of 37 Genome

“2012BAK11B05”, AQSIQ support program “2015IK067, 2015IK069” Guangdong Province

support program “2015A050502009”.

References Adamowicz S. J. 2015. International Barcode of Life: Evolution of a global research community. Genome 58(5): 151-162. Barcaccia G., Lucchin M., Cassandro M. 2016. DNA barcoding as a molecular tool to track down mislabeling and food piracy. Diversity 8(1): 2. Bunchu, N., Sukontason, K.L., Olson, J.K., Kurahashi, H., and Sukontason, K. 2007. Behavioral responses of Chrysomya megacephala to natural products. Parasitology Research 102 (3): 419–429. Butcher, B.A., Smith, M.A., Sharkey, M.J., and Quicke, D.L.J. 2012. A turbo-taxonomic study of Thai Aleiodes (Aleiodes ) and Aleiodes ( Arcaleiodes ) (Hymenoptera: Braconidae: Rogadinae) based largely on CO1 barcoded specimens, with rapid descriptions of 179 new species. Zootaxa 3457 : 1–232. Cai, J.F., Liu, M., Ying, B.W., Deng, R.L., Dong, J.G., Zhang, L., Tao, T., Pan, H.F., Yan, H.T., and Liao, Z.G. 2005. The availability of mitochondrial DNA cytochrome oxidase I gene for the distinction of forensically important flies in China. Acta Entomologica Sinica 48 (3): 380–385. Cawthorn D. M., Steinman H. A., Witthuhn R. C. 2012. DNA barcoding reveals a high incidence of fish species misrepresentation and substitutionDraft on the South African market. Food Research International 46(1): 30-40. DeSalle, R., Egan, M.G., and Siddall, M.E. 2005. The unholy trinity: taxonomy, species delimitation and DNA barcoding. Proceedings of the Royal Society of London Series B: Biological Sciences 360 (1462): 1905–1916. doi:10.1098/rstb.2005.1722. Ebach, M.C. 2011. Taxonomy and the DNA barcoding enterprise. Zootaxa 2742 : 67–68. Engstrand, R.C., Tovar, J.C., Cibrián-Jaramillo, A., and S-O, K. 2010. Genetic variation in avocado stem weevils Copturus aguacatae (Coleoptera: Curculionidae) in Mexico. Mitochondrial DNA 21 (S1): 38–43. Fan, Z.D. 1992. Key to Chinese common flies. Science Press. Beijing. Ferri, G., Alu, M., Corradini, B., and Beduschi, G. 2009. Forensic botany: species identification of botanical trace evidence using a multigene barcoding approach. International Journal of Legal Medicine 123 (5): 395–401. doi:10.1007/s00414-009-0356-5. Folmer, O., Black, M., Hoeh, W., Lutz, R., and Vrijenhoek, R. 1994. DNA Primers for amplification of Mitochondrial cytochrome C oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology Biotechnology 3(5): 294–299. Gattolliat, J.L., Cavallo E., Vuataz, L., Sartori. M. 2015. DNA barcoding of Corsican mayflies (Ephrmeroptera) with impliations on biogeography, systematics and biodiversity. Arthropod Systematics and Phylogency 73(1)3-18. Goff, M.L. 2001. A Fly for the Prosecution: How insect evidence helps solve crimes. Harvard University Press, Massachusetts. Grant, B. 2007. Cataloging life. The Scientist 21(12):36. Available online at: http://www.the-scientist.com/article/display/53881.

Hebert, P.D.N., Cywinska, A., Ball, S.L., and de Waard, J.R. 2003. Biological identifications through DNA barcodes. Proceedings of the Royal Society of London Series B: Biological Sciences 270 : 313–321.

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doi:10.1098/rspb.2002.2218 Hebert, P.D.N., Penton, E.H., Burns, J.M., Janzen, D.H., and Hallwachs, W. 2004. Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astrapes fulgerator . Proceedings of the National Academy of Science of the United States of America 101 (41): 14812–14817. doi:10.1073/pnas.0406166101. Hendrich, L., and Balke, M. 2011. A simultaneous journal/wiki publication and dissemination of a new species description: Neobidessodes darwiniensis sp. n. from northern Australia (Coleoptera, Dytiscidae, Bidessini). ZooKeys 79 : 11–20. doi:10.3897/zookeys.79.803. Hernandez-Triana L.M. 2015. DNA barcoding of Neotropical black flies (Diptera:Simuliidae): Species identification and discovery of cryptic diversity in Mesoamerica. Zootaxa 3936(1):93-114. Imbiriba, A.S., Izutani, D.T., Milhoretto, I.T., and Luz, E. 1977. Introdução da Chrysomya chloropyga (Wiedemann, 1818) na região neotropical (Diptera, Calliphoridae). Archivos de biologia e tecnologia Curitiba 20 : 35–39. Klausnitzer, B. 2010. Entomologie - quo vadis? Nachrichtenblatt der Bayerischen Entomologen 59 : 99 –111. Leigh, J.W., and Bryant, D. 2015. PopART: Full-feature software for haplotype network construction. Methods in Ecology and Evolution 6(9): 1110–1116. doi:10.1111/2041-210X.12410. Liao, J., Yue, Q., Qiu, D., Wei, X., Liu, D., and Jia, F. 2014. Morphology and DNA barcoding of a newly intercepted fly species exotic in china, Calliphora dubia (Macquart, 1855). Chin J Vector Biol & Control 25 (6): 509–513. Draft Liu, D., Nie, W., Qiu, D., Guo, Z., Wei, X., Chen, J., Hu, J., and Yue, Q. 2014. DNA Barcoding Identification of Unknown Pupa Intercepted from Entry Ship. Journal of inspection and quarantine 24 (5): 53–57. Luo, X.R. 1993. Practical Color Atlas for Chinese traditional medicine. Guangdong Sciences and Technology Press, Guangzhou. Meiklejohn, K.A., Wallman, J.F., and Dowton, M. 2011. DNA-based identification of forensically important Australian Sacrophagidae (Diptera). International Journal of Legal Medicine 125 (1): 27–32. doi:10.1007/s00414-009-0395-y. Meyer, C. P., and Paulay, G. 2005. DNA barcoding: error rates based on comprehensive sampling. PLoS Biology, 3(12), e422. doi:10.1371/journal.pbio.0030422. Neigel, J., Domingo, A., and Stake, J. 2007. DNA barcoding as a tool for coral reef conservation. Coral Reefs 26 (3): 487–499. Nelson, L.A., Wallman, J.F., and Dowton, M. 2007. Using COI barcodes to identify forensically and Medically important blowflies. Medical and Veterinary Entomology 21 : 44–52. Nelson, L.A., Lambkin, C.L., Batterham, P., Wallman, J.F., Dowton, M., Whiting, M.F., Yeates, D.K., Cameron, S.L. 2012. Beyond barcoding: A mitochondrial genomics approach to molecular phylogenetics and diagnostics of blowflies ( Diptera: Calliphoridae). Gene http://dx.doi.org/10.1016/j.gene.2012.09.103. Ramaraj, P., Chitra, S., Veeramani, V., Ganesh, A., Janarthanan, S. 2014. Resdescription and DNA barcoding of Synanthropic derived form of Chrysomya megacephala (Diptera: Calliphoridae) from dacaying fish in Tamil Nadu, South India. Interantional conference on Entomology. doi:13140/2.1.3389.0888. Ratnasingham, S., and Hebert, P.D.N. 2007. The Barcode of Life Data System (www.barcodinglife.org). Molecular Ecology Notes 7(3): 355-364. doi:10.1111/j.1471-8286.2007.01678.x. gen-2015-0174.R2 14 https://mc06.manuscriptcentral.com/genome-pubs Page 15 of 37 Genome

Raupach, M.J., Hendrich, L., Küchler, S., Deister, F., Morinière, J., and Gossner, M.M. 2014. Building-Up of a DNA Barcode Library for True Bugs (Insecta: Hemiptera: Heteroptera) of Germany Reveals Taxonomic Uncertainties and Surprises. PLoS ONE 9(9): e106940. doi:106910.101371/journal.pone.0106940. doi:10.1371/journal.pone.0106940. Renaud, A.K., Savage, J., and Adamowicz, S.J. 2012. DNA barcoding of Northern Nearctic Muscidae (Diptera) reveals high correspondence between morphological and molecular species limits. BMC Ecology 12 : 24. doi:10.1186/1472-6785-12-24. Riedel, A., Sagata, K., Surbakti, S., Tänzler, R., and Balke, M. 2013. One hundred and one new species of Trigonopterus weevils from New Guinea. Zookeys 280 : 1–150. doi:10.3897/zookeys.280.3906. Rivera, J., and Currie, D. 2009. Identification of Nearctic black flies using DNA barcodes (Diptera: Simullidae). Molecular Ecology Resources 9(S1): 224–236. doi:10.1111/j.1755-0998.2009.02648.x. Salem. A.M., Adham, F.K., Picard, C.J. 2015. Survey of the genetic diversity of forensically important Chrysomya (Diptera:Calliphoridae) from Egypt. Journal of Medical Entomology Doi:http://dx.doi.org/10.1093/jme/tjv013 320-328. Schuehli, G.S.E., de Carvalho, C.J.B., and Wiegmann, B.M. 2007. Molecular phylogenetics of the Muscidae (Diptera: Calyptratae): new ideas in a congruence context. Invertebrate Systematics 21 : 263–278. Shi, Y.W., Liu, X.S., Wang, H.Y., and Zhang, Y.J. 2008. Study of living habits of Chrysomya megacephala and its forensic application. Acta Scientiarum Naturalium University Sunyatsen S1 (47): 70–76. Draft Sing, K.W., Sofian-Azirun, M., and Tayyab, S. 2012. Protein analysis of Chrysomya megacephala maggot meal. Animal Nutriution and Feed Technology 12 (1): 35–46. Stoev, P., Akkari, N., Zapparoli, M., Porco, D., Enghoff, H., Edgecombe, G.D., Georgiev, T., and Penev, L. 2010. The centipede genus Eupolybothrus Verhoef, 1907 (Chilopoda: Lithobiomorpha: Lithobiidae) in North Africa, a cybertaxonomic revision, with a key to all species in the genus and the first use of DNA barcoding for the group. ZooKeys 50 : 29–77. doi:10.3897/zookeys.50.504. Sukontason, K.L., Bunchoo, M., Khantawa, B., Piangjai, S., Rongsiyam, Y., and Sukontason, K. 2007. Comparation between Musca domestica and Chrysomya megacephala as carriers of bacteria in northern Thailand. Southest Asian Journal of Tropical Medicine and Public Health 38 (38–44). Taha, N., Abdel-Meguid, A., and El-ebiarie, A. 2010. Application of active excretory/secretory products from third larval instar of Chrysomya megacephala (Diptera: Calliphoridae) on an artificial wound. Journal of American Sciences 6(7): 313–317. Tamura, K., Stecher, G., Peterson, D., Filipski, A., and Kumar, S. 2013. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Molecular Biology and Evolution 30 (12): 2725–2729. doi:10.1093/molbev/msr121. Wall, R., Howard, J.J., and Bindu, J. 2001. The seasonal abundance of blowflies infesting drying fish in south-west India. Journal of Applied Ecology 38 (2): 339–348. Wall, R., and Shearer, D. 1997. Veterinary Entomology: Arthropod Ectoparasites of Veterinary Importance. Springer, London. Ward, R.D., Homes, B.H., White, W.T., and Last, P.R. 2008. DNA barcoding Australasian chondrichthyans: results and potential uses in conservation. Marine and Freshwater Research 59 (1): 57–71. doi:10.1071/MF07148. Webb J.M., Jacobus L.M., Funk D.H., Zhou X., Kondratieff B., Geraei C.J., Dewalt R.E., Baird D.J., Richard B., Phillips I., Hebert P.D. 2012. A DNA barcode library for North American Ephemeroptera:

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progress and prospects. Plos One 7(5): e38063.doi:10.1371/journal.pone.0038063. Wei, X., Qiu, D., Yue, Q., and Guo, Z. 2014. DNA Barcoding Identification of China Non-Recorded Mosquito Species Intercepted at the Port. Journal of Inspection and quarantine 24 (6): 46–49+67. Wells, J.D. 1991. Chrysomya megacephala (Diptera: Calliphoridae) has reached the continental United States: review of its biology, pest status, and spread around the world. Journal of Medical Entomology 28 (3): 471–473. doi:10.1093/jmedent/28.3.471. Wesener, T. 2012. Nearctomeris , a new genus of pill millipedes from North America, with a comparison of genetic distances of American pill millipede genera (Glomerida, Glomeridae). Zootaxa 3258 : 58–68. Wesener, T., Raupach, M.J., and Decker, P. 2011. Mountain refugia play a role in soil arthropod speciation on Madagascar: a case study of the endemic Giant fire millipede genus Aphisto goniulus . Public Library of Science ONE 6(12): e28035. doi:28010.21371/journal.pone.0028035. doi:10.1371/journal.pone.0028035. Will, K.P., Mishler, P.D., and Wheeler, Q.D. 2005. The perils of DNA barcoding and the need for integrative taxonomy. Systematic Biology 54 (5): 844–851. Williams, K.A., and Villet, M.H. 2006. A new and earlier record of Chrysomya megacephala in South Africa, with notes on another exotic species, Calliphora vicina (Diptera: Calliphoridae). African Invertebrates 47 : 347–350. Wong, H.K., and Hanner, R.H. 2008 DNA barcoding detects market substitution in North American seafood. Food Research International 41 :828-837. Wu, S.Y., and Hu, M. 2012. Advances Draftin research on Chrysomya megacephala (Fabricuis) in China. Chinese Journal of Vector Biology and Bontrol 4: 370–373. Xue, W.Q., and Zhao, J.M. 1996. Flies of China. Liaoning Sciences and Technology Press, Liaoning. Yang, S.T.,and Shiao,S.F 2014 Temperature adaptation in Chrysomya megacephala and Chrysomya pinguis , two blow fly species of forensic significance. Entomologia Experimentalis et Applicata 152(2):100-107. Yue, Q., Qiu, D., Hu, J., and Liu, G. 2013. DNA Barcoding-A Novel Tool for Fast and Accurate Identification of Medical Vectors. Journal of inspection and quarantine 23 (5): 60–63+49. Yue, Q., Wu, K., Qiu, D., Hu, J., Liu, D., Wei, X., Chen, J., and Cook, C.E, 2014. A Formal Re-Description of the Cockroach Hebardina concinna Anchored on DNA Barcodes Confirms Wing Polymorphism and Identifies Morphological Characters for Field Identification. PLoS ONE 9: e106789. doi:106710.101371/journal.pone.0106789.

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Table 1. Species and number of individuals for which mitochondrial COI barcodes were

sequenced.

Family Sub-family Genus Specific No. of epithet individuals Calliphoridae Chrysomyinae Chrysomya megacephala 208 pinguis 36 Achoetandrus rufifacies 34 Phormiinae Protophormia terraenovae 13 Calliphorinae Lucilia illustris 10 cuprina 36 sericata 47 Hemipyrellia ligurriens 41 Muscidae Muscinae DraftMusca domestica 50 sorbens 33 Coenosiinae Graphomya rufitibia 40 Sarcophagidae Sarcophaginae Sarcophaga albiceps 33 brevicornis 32 peregrina 32

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Figure captions

Fig. 1. Collecting localities of Chrysomya megacephala and other Diptera in China. The 42 solid circles are the localities where C. megacephala were found, the ten empty circles are the localities, at high altitude on the Tibetan Plateau, in arid areas near the Mongolian border, and in low temperature areas near the Russian border, where we searched for but did not find C. megacephala . Map data ©2015 Google.

Fig. 2. Kimura two-parameter (K2P) pairwise sequence distances of the 658 bp COI barcoding region between 208 individuals of Chrysomya megacephala, 36 C. pinguis, and 13 Protophormia terraenovae (Cmeg, Cpin, and Pter) collected in China. These included nine individuals intercepted at the port of Zhongshan: six from Lima, Peru and three from Manila, the Philippines as those individuals were collected in China. The maximum K2P distance between two C. megacephala is 0.011, while the minimum between C. megacephala and C. pinguis is 0.016. The mean Cmeg/Cmeg distance is 0.0028, while the mean Cmeg/Cpin distance is 7.9-fold greater at 0.0220. It is clear that sequences of the COI barcode region are sufficient to distinguish biological material from these two species. Distances to P. terraenovae are considerably greater, as were pairwise distances for other collected fliesDraft (data not shown).

Fig. 3. Minimum spanning network diagrams for Chrysomya megacephala for the 658 bp COI barcode region. Minimum spanning networks were created using PopART, with epsilon of 0. Both data sets are robust in that nearly identical topologies are produced regardless of which network algorithm is used. (a) Network showing 208 sequences collected in China. Collection sites were assigned to one of seven regions within China and encoded in a nexus traits block. The nine sequences intercepted at the port of Zhongshan (six from Lima, three from Manila), all sharing the same most common haplotype, were assigned to the Fujian-Guangzhou-Guangxi region where they were collected. Numbers next to regional name indicate number of sequences from that region. Each circle represents one or more identical sequences, with circle size proportional to the number of sequences. Numbers beside larger circles indicate number of sequences within that group. Branch lengths and angles are arbitrary: each hash line across a branch indicates a single mutation. The maximum path length across the network is 11 mutations. Colors indicate geographic origin of the sequences within each group. Sequences from northern and central China (the top five regions in the key) occur throughout the network. However, individuals from Hainan occur only within, and branching from, the 97-sequence group, while individuals from Sichuan and Yunnan occur only within the 97-sequence group and branching from the 11-sequence group at the top center. (b) Network showing 306 C. megacephala sequences; 208 as in (a) plus 98 additional sequences from the Barcode of Life Data Systems (Ratnasingham and Hebert 2007). These sequences included six additional individuals from China, but for this network we assigned the six sequences originating in Manila and the six originating in Lima, but intercepted at Zhongshan, to their country of origin as representatives of genotypes that are present in the Philippines and Peru. Again, all nine share the most common haplotype. As in (a) individuals from all countries are present in the largest haplogroup. The second largest haplogroup is limited to flies gen-2015-0174.R2 18 https://mc06.manuscriptcentral.com/genome-pubs Page 19 of 37 Genome

from China, Malaysia, and Egypt. Most of the variation in the data is within the flies we collected in China, as expected given the larger relative sample size and the widespread collection locations.

Draft

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Draft

Fig. 1. Collecting localities of Chrysomya megacephala and other Diptera in China. The 42 solid circles are the localities where C. megacephala were found, the ten empty circles are the localities, at high altitude on the Tibetan Plateau, in arid areas near the Mongolian border, and in low temperature areas near the Russian border, where we searched for but did not find C. megacephala. Map data ©2015 Google.

128x91mm (300 x 300 DPI)

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Draft

Fig. 2. Kimura two-parameter (K2P) pairwise sequence distances of the 658 bp COI barcoding region between 208 individuals of Chrysomya megacephala, 36 C. pinguis, and 13 Protophormia terraenovae (Cmeg, Cpin, and Pter) collected in China. These included nine individuals intercepted at the port of Zhongshan: six from Lima, Peru and three from Manila, the Philippines. The maximum K2P distance between two C. megacephala is 0.011, while the minimum between C. megacephala and C. pinguis is 0.016. The mean Cmeg/Cmeg distance is 0.0028, while the mean Cmeg/Cpin distance is 7.9-fold greater at 0.0220. It is clear that sequences of the COI barcode region are sufficient to distinguish biological material from these two species. Distances to P. terraenovae are considerably greater, as were pairwise distances for other collected flies (data not shown).

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Draft

Minimum spanning network diagrams for Chrysomya megacephala for the 658 bp COI barcode region. Minimum spanning networks were created using PopART, with epsilon of 0. Both data sets are robust in that ne arly identical topologies are produced regardless of which network algorithm is used. (a) Network showing 208 sequences collected in China. Collection sites were assigned to one of seven regions within China and encoded in a nexus traits block. The nine sequences intercepted at the port of Zhongshan (six from Lima, three from Manila), all sharing the same most common haplotype, were assigned to the Fujian-Guangzhou- Guangxi region where they were collected. Numbers next to regional name indicate number of sequences from that region. Each circle represents one or more identical sequences, with circle size proportional to the number of sequences. Numbers beside larger circles indicate number of sequences within that group. Branch lengths and angles are arbitrary: each hash line across a branch indicates a single mutation. The maximum path length across the network is 11 mutations. Colors indicate geographic origin of the sequences within each group. Sequences from northern and central China (the top five regions in the key) occur throughout the network. However, individuals from Hainan occur only within, and branching from, the 97-sequence group, while individuals from Sichuan and Yunnan occur only within the 97-sequence group and branching from the 11-sequence g roup at the top center. (b) Network showing 306 C. megacephala sequences; 208 as

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in (a) plus 98 additional sequences from the Barcode of Life Data Systems (Ratnasingham and Hebert 2007). These sequences included six additional individuals from China, but f or this network we assigned the six sequences originating in Manila and the six originating in Lima, but intercepted at Zhongshan, to their country of origin as representatives of genotypes that are present in the Philippines and Peru. Again, all nine share the most common haplotype. As in (a) individuals from all countries are present in the largest haplogroup. The second largest haplogroup is limited to flies from China, Malaysia, and Egypt. Most of the variation in the data is within the flies we collected in China, as expected given the larger relative sample size and the widespread collection locations.

209x244mm (300 x 300 DPI)

Draft

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Table S1. Publicly available Chrysomya megacephala COI barcode sequences downloaded from the Barcode of Life Data Systems (BOLD) public data portal. BOLD accession number, GenBank accession number, and country of collection are listed.

BOLD accession GenBank accession Country GBDP14020-13 NC_019633 Australia GBDP14083-13 JX913739 Australia GBDP14084-13 JX913738 Australia GBDP3477-07 DQ647353 Australia GBDP3478-07 DQ647352 Australia GBDP3479-07 DQ647351 Australia GBDP3480-07 DQ647350 Australia GBDP15509-14 KJ195707 Brazil GBDP15510-14 KJ195708Draft Brazil GBDP15512-14 KJ195714 Brazil GBDP0975-06 AY092761 China GBDP14428-13 KF037970 China GBDP14429-13 KF037969 China GBDP9050-10 FJ614818 China GBDP9051-10 FJ614817 China GBDP9052-10 FJ614816 China GBDP15505-14 KC249673 Egypt GBDP15506-14 KC249674 Egypt GBDP15507-14 KC249675 Egypt GBDP15508-14 KC249676 Egypt GBDP0583-06 AF295551 India GBDP15222-14 AB907185 India GBDP15230-14 AB910389 India GBDP15231-14 AB910390 India GBDP2900-07 AJ426041 India SPLID013-13 India SPLID033-14 India GBDP13116-13 KC855286 Malaysia GBDP13130-13 KC855272 Malaysia GBDP13131-13 KC855271 Malaysia GBDP13132-13 KC855270 Malaysia GBDP15270-14 KF562106 Malaysia

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GBDP15513-14 KJ496781 Malaysia GBDP15514-14 KJ496782 Malaysia GBDP15515-14 KJ496783 Malaysia GBDP15516-14 KJ496784 Malaysia GBDP15517-14 KJ496785 Malaysia GBDP1869-06 AY909052 Malaysia GBDP1870-06 AY909053 Malaysia GBMIN21190-13 JX187374 Malaysia GBMIN21191-13 JX187372 Malaysia GBMIN21192-13 JX187370 Malaysia GBMIN21193-13 JX187368 Malaysia GBMIN21319-13 JX187373 Malaysia GBMIN21320-13 JX187371 Malaysia GBMIN21321-13 JX187369 Malaysia GBMIN22942-13 JX027581 Malaysia GBMIN22943-13 JX027579 Malaysia GBMIN22944-13 JX027577 Malaysia GBMIN22945-13 JX027575 Malaysia GBMIN22946-13 JX027573Draft Malaysia GBMIN22947-13 JX027571 Malaysia GBMIN22948-13 JX027569 Malaysia GBMIN22949-13 JX027567 Malaysia GBMIN22950-13 JX027565 Malaysia GBMIN22951-13 JX027563 Malaysia GBMIN22952-13 JX027561 Malaysia GBMIN22953-13 JX027559 Malaysia GBMIN22954-13 JX027557 Malaysia GBMIN22955-13 JX027555 Malaysia GBMIN22956-13 JX027553 Malaysia GBMIN22957-13 JX027551 Malaysia GBMIN22958-13 JX027549 Malaysia GBMIN22961-13 JX027580 Malaysia GBMIN22962-13 JX027578 Malaysia GBMIN22963-13 JX027576 Malaysia GBMIN22964-13 JX027574 Malaysia GBMIN22965-13 JX027572 Malaysia GBMIN22966-13 JX027570 Malaysia GBMIN22967-13 JX027568 Malaysia GBMIN22968-13 JX027566 Malaysia GBMIN22969-13 JX027564 Malaysia GBMIN22970-13 JX027562 Malaysia GBMIN22971-13 JX027560 Malaysia GBMIN22972-13 JX027558 Malaysia

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GBMIN22973-13 JX027556 Malaysia GBMIN22974-13 JX027554 Malaysia GBMIN22975-13 JX027552 Malaysia GBMIN22976-13 JX027550 Malaysia GBMIN22977-13 JX027548 Malaysia GBMIN30954-13 JN229003 Malaysia GBMIN30956-13 JN228999 Malaysia GBMIN30958-13 JN228995 Malaysia GBMIN30961-13 JN229000 Malaysia GBMIN30963-13 JN228996 Malaysia GBMIN30964-13 JN228994 Malaysia GBMIN32602-13 JN571566 Malaysia GBMIN32603-13 JN571564 Malaysia GBMIN32604-13 JN571562 Malaysia GBMIN32607-13 JN571556 Malaysia GBMIN32608-13 JN571554 Malaysia GBMIN32614-13 JN571561 Malaysia GBMIN32617-13 JN571555 Malaysia GBMIN32618-13 JN571553Draft Malaysia DIRTT059-11 KC617813 United States DIRTT060-11 KC617814 United States DIRTT061-11 KC617812 United States GBMIN18761-13 JQ246662 United States

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Table S2Collection localities, geographic information, and COI barcode region GenBank

accession numbers for each individual fly.

Collection Province GPS Coordinates Internal GenBank Collecto Collection Date Species Locality Long.(E) Lat. (N) specimen ID Accession No. r (yyyymmdd)

Chaohu Anhui 117.872 31.642 28Ad-1 KJ129130 Wang 20121002 28Ad-2 KJ129131 XD 28Ad-3 KJ129132 Luan 116.333 31.393 28P-1 KJ129098 Wang 20120903 28P-2 KJ129099 XD 28P-3 KJ129100

Chrysomya megacephala Chongqing Chongqing 106.429 29.821 28T-1 KJ129110 Liu DX 20130725 28T-2 KJ129111 28T-3 KJ129112 Fuzhou Fujian 119.300 26.150 28I-1 KJ129080 Wei XY 20120928 28I-2 KJ129081 28I-3 KJ129082 28I-4 KJ129083 Xiamen 118.176 24.518 28G-1 KJ129073 Wang 20120918 28G-2 KJ129074 XD 28G-3 KJ129075 28G-4 KJ129076 Wuyishan 118.004 27.705 28H-1 KJ129077 Wei XY 20120923 28H-2 KJ129078 28H-3 KJ129079 Huizhou Guangdong 114.509 23.177 28D-1 KJ129062 Yue QY 20120519 28D-2 KJ129063 Draft28D-3 KJ12906, 28D-4 KJ129065 Meizhou 116.089 24.271 28Q-1 KJ129101 Qiu DY 20121004 28Q-2 KJ129102 28Q-3 KJ129103 Shantou 116.712 23.403 28B-1 KJ129054 Yue QY 20120518 28B-2 KJ129055 28B-3 KJ129056 28B-4 KJ129057 Yunfu 112.059 22.912 28C-1 KJ129058 Liu DX 20121001 28C-2 KJ129059 28C-3 KJ129060 28C-4 KJ129061 Zhongshan 113.423 22.517 28A-1 KJ129053 Huang 20110315 28A-2 KJ145953 YW 26A KP310058 20110823 Fangchenggang Guangxi 108.055 21.892 28Aa-1 KJ129133 Wang 20130407 28Aa-2 KJ129134 XD Chongzuo 106.961 22.468 26O-1 KP408518 Wu KL 20140505 26O-2 KP408519 26O-3 KP408520 26P-1 KP408521 26P-2 KP408522 26P-3 KP408523 Haikou Hainan 110.316 20.034 28M-1 KJ129090 Wang 20121126 28M-2 KJ129091 XD 28M-3 KJ129092 Ledong 108.863 18.740 28N-1 KJ129093 Wang 20130429 28N-2 KJ129094 XD Sanya 109.508 18.256 28K-1 KJ129085 Wang 20121120 28K-2 KJ145954 XD Wuzhishan 109.671 18.880 28L-1 KJ129086 Wang 20121124 28L-2 KJ129087 XD 28L-3 KJ129088 28L-4 KJ129089 Shijiazhuang Hebei 114.411 38.070 28Ab-1 KJ129125 Liu DX 20120822 28Ab-2 KJ145956

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28Ab-3 KJ129126 Qinhuangdao 119.485 39.835 28F-1 KJ129069 Wang 20120918 28F-2 KJ129070 XD 28F-3 KJ129071 28F-4 KJ129072 Zhengzhou Henan 113.673 34.900 28W-1 KJ129119 Qiu XY 20130812 28W-2 KJ129120 28W-3 KJ129121 Yueyang Hunan 113.092 29.373 28Z-1 KJ129122 Chen J 20130821 28Z-2 KJ129123 28Z-3 KJ129124 Xiangyang Hubei 112.178 32.045 28Y-1 KJ129141 Chen J 20130819 28Y-2 KJ129142 28Y-3 KJ129143 28Y-4 KJ129144 28Y-5 KJ129145 Xiaogan 114.120 31.556 28J-1 KJ129084 Wei XY 20120930 28J-2 KJ145955 Hohhot Inner 111.653 40.752 28U-1 KJ129113 Hu J 20130804 Mongolia 28U-2 KJ129114 28U-3 KJ129115 Nanjing Jiangsu 118.746 32.087 28O-1 KJ129095 Liu DX 20120831 28O-2 KJ129096 28O-3 KJ129097 Jinan Shandong 117.025 36.675 28Ac-1 KJ129127 Wang 20120823 28Ac-2 KJ129128 XD 28Ac-3 KJ129129 Weinan Shaanxi 109.430Draft 34.517 28X-1 KJ129136 Chen J 20130817 28X-2 KJ129137 28X-3 KJ129138 28X-4 KJ129139 28X-5 KJ129140 Datong Shanxi 113.190 40.106 28V-1 KJ129116 Chen J 20130810 28V-2 KJ129117 28V-3 KJ129118 Emeishan Sichuan 103.493 29.591 28S-1 KJ129107 Wu KL 20130722 28S-2 KJ129108 28S-3 KJ129109 Panzhihua 101.636 26.711 28R-1 KJ129104 Wu KL 20130716 28R-2 KJ129105 28R-3 KJ129106 Tianjin Tianjin 117.488 40.022 28E-1 KJ129066 Wang 20120816 28E-2 KJ129067 XD 28E-3 KJ129068 Botanical Liaoning 121.659 38.909 26E-1 KP408488 Liu DX 20140828 Garden,Dalian 26E-2 KP408489 26E-3 KP408490 26F-1 KP408491 26F-2 KP408492 26F-3 KP408493 26G-1 KP408494 26G-2 KP408495 26G-3 KP408496 26H-1 KP408497 26H-2 KP408498 26H-3 KP408499 26I-1 KP408500 26I-2 KP408501 26I-3 KP408502 26J-1 KP408503 26J-2 KP408504 26J-3 KP408505 Fujiazhuang 121.623 38.865 26K-1 KP408506 Chen J 20140829 Park, Dalian 26K-2 KP408507 26K-3 KP408508

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26L-1 KP408509 26L-2 KP408510 26L-3 KP408511 26M-1 KP408512 26M-2 KP408513 26M-3 KP408514 26N-1 KP408515 26N-2 KP408516 26N-3 KP408517 Xixi National Zhejiang 120.067 30.269 26Q-1 KP408524 Liao JL 20140825 Wetland 26Q-2 KP408525 Park,Hangzhou 26Q-3 KP408526 Precious Stone 120.144 30.261 26R-1 KP408527 20140826 Hill,Hangzhou 26R-2 KP408528 26R-3 KP408529 Tianmushan,Han 119.429 30.348 26S-1 KP408530 20140822 gzhou 26S-2 KP408531 Longshan park, 119.597 28.625 26U-1 KP408535 Wei XY 20140829 Jinhua 26U-2 KP408536 26U-3 KP408537 26V-1 KP408538 20140827 26V-2 KP408539 26V-3 KP408540 Moon 119.668 29.086 26W-1 KP408541 20140831 garden,Jinhua 26W-2 KP408542 26W-3 KP408543 Huangbinhong 119.652 29.096 26X-1 KP408544 20140901 park, Jinhua 26X-2 KP408545 Draft26X-3 KP408546 Jiujiang Jiangxi 116.003 29.711 26T-1 KP408532 Wu KL 20140907 26T-2 KP408533 26T-3 KP408534 Harbin Heilongjiang 127.155 45.567 26Z-1 KP408547 Chen J 20140806 26Z-2 KP408548 26Z-3 KP408549 Longfeng marsh 125.096 46.515 19C-1 KP408460 Liu DX 20140818 Park,Daqing 19C-2 KP408461 19C-3 KP408462 Times square, 125.101 46.582 19D-1 KP408463 20140817 Daqing 19D-2 KP408464 19D-3 KP408465 People's Park, 129.627 44.588 19E-1 KP408466 Chen J 20140822 Mudanjiang 19E-2 KP408467 19E-3 KP408468 Jiangdong, 129.119 44.113 19F-1 KP408469 20140820 Mudanjiang 19F-2 KP408470 Heihe 126.170 48.652 19G-1 KP408471 Yue QY 20140816 19G-2 KP408472 19G-3 KP408473 19H-1 KP408474 19H-2 KP408475 19H-3 KP408476 Longsha Park, 123.944 47.344 19K-1 KP408483 20140813 Qiqihar 19K-2 KP408484 Peace Square, 123.919 47.360 19L-1 KP408485 20140812 Qiqihar 19L-2 KP408486 19L-3 KP408487 Jilin Jilin 126.702 43.721 19A-1 KP408454 Chen J 20140826 19A-2 KP408455 19A-3 KP408456 19B-1 KP408457 19B-2 KP408458 19B-3 KP408459 Hailan riverside, 129.427 42.787 19I-1 KP408477 Liu DX 20140823 Yanji 19I-2 KP408478 19I-3 KP408479

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Hailan Lake, 129.633 42.913 19J-1 KP408480 20140824 Yanji 19J-2 KP408481 19J-3 KP408482 ------Manila, ------26B-1 KP310059 Nie WZ 20140308 Philippines 26B-2 KP310060 26B-3 KP310061 26C-1 KP310062 26C-2 KP310063 26C-3 KP310064 26Y-1 KP310068 26Y-2 KP310069 26YP-2 KP310070 ------Lima, Peru ------28Ae-1 KP310055 Nie WZ 20140307 28Ae-2 KP310056 28Ae-3 KP310057 26D-1 KP310066 26D-2 KP310067 26D-3 KP310068 Zhongshan Guangdong 113.423 22.517 199C-1 KJ129510 Wei XY 20130304

199C-2 KJ129511 199C-3 KJ129512

Chrysomya pinguis Fangchenggang Guangxi 108.055 21.892 199D-1 KJ129513 Wang 20130407 199D-2 KJ129514 XD 199D-3 KJ129515 Zunyi Guizhou 107.191 27.937 199RS-1 KJ129525 Liu DX 20130801 199RS-2 KJ129526 199RS-3 KJ129527 Shijiazhuang Hebei 114.353Draft 37.909 199K-1 KJ129507 Liu DX 20120822 199K-2 KJ129508 199K-3 KJ129509 Zhengzhou Henan 113.673 34.900 199I-1 KJ129528 Chen J 20130812 199I-2 KJ129529 199I-3 KJ129530 Jiaozuo 113.386 35.421 199J-1 KJ129531 Hu J 20130813 199J-2 KJ129532 199J-3 KJ129533 Datong Shanxi 113.142 40.114 199Aa-1 KJ129540 Qiu XY 20130809 199Aa-2 KJ129541 199Aa-3 KJ129542 Panzhihua Sichuan 101.636 26.711 199O-1 KJ129522 Liu DX 20130816 199O-2 KJ129523 199O-3 KJ129524 Bayi, Tibet 94.343 29.664 199E-1 KJ129534 Wu KL 20130702 Nyingchi 199E-2 KJ129535 199E-3 KJ129536 Paizhen, 94.389 29.623 199Ee-1 KJ129537 Liu DX 20130705 Nyingchi 199Ee-2 KJ129538 199Ee-3 KJ129539 199Ee-4 KJ129516 Wu KL 20130705 199Ee-5 KJ129517 199Ee-6 KJ129518 Motuo, 95.333 29.325 199F-1 KJ129519 Liu DX 20130709 Nyingchi 199F-2 KJ129520 199F-3 KJ129521 Luan Anhui 116.255 31.360 76C-1 KJ129261 Wang 20120903 XD Fuzhou Fujian 119.290 26.093 76G-1 KJ129271 Wang 20120926 76G-2 KJ129272 XD 76G-3 KJ129273 Xiamen 118.176 24.518 76E-1 KJ129265 Wang 20120918

Achoetandrus rufifacies 76E-2 KJ129266 XD 76E-3 KJ129267 Wuyishan 117.986 27.625 76F-1 KJ129268 Wang 20120925 76F-2 KJ129269 XD

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76F-3 KJ129270 Shantou Guangdong 116.712 23.403 76D-1 KJ129262 Yue QY 20120518 76D-2 KJ129263 76D-3 KJ129264 Yunfu 112.058 22.941 76H-1 KJ129274 Wang 20121021 76H-2 KJ129275 XD 76H-3 KJ129276 Zhongshan 22.517 76A-1 KJ129257 Yue QY 20121023 113.423 76A-2 KJ129283 76A-3 KJ129284 76A-4 KJ129285 Haikou Hainan 110.316 20.034 233C-1 KJ129289 Wang 20121126 233C-2 KJ129290 XD 233C-3 KJ129291 Wuzhishan 109.523 18.789 233B-1 KJ129286 Wang 20121122 233B-2 KJ129287 XD Xiangyang Hubei 112.109 32.035 76J-1 KJ129280 Hu J 20130819 76J-2 KJ129281 76J-3 KJ129282 Nanjing Jiangsu 118.746 32.087 76B-1 KJ129258 Liu DX 20120829 76B-2 KJ129259 76B-3 KJ129260 Emeishan Sichuan 103.493 29.591 76I-1 KJ129277 Liu DX 20130722 76I-2 KJ129278 76I-3 KJ129279 Zhongshan Guangdong 113.423 22.517 97A-1 KJ129244 Guan W 20111201 97A-2 KJ129245 97A-3 KJ129246, Draft97A-4 KJ129247 Hohhot Inner 111.229 41.323 97C-1 KJ129251 Qiu XY 20130805 Mongolia 97C-2 KJ129252 97C-3 KJ129253 Protophormia terraenovae Erlianhot 111.962 43.657 97D-1 KJ129254 Hu J 20130807 97D-2 KJ129255 97D-3 KJ129256 Paizhen, Tibet 94.213 29.216 97B-1 KJ129248 Wu KL 20130703 Nyingchi 97B-2 KJ129249 97B-3 KJ129250

Chaohu Anhui 117.872 31.642 174L-1 KJ129408 Wang 20121002 174L-2 KJ129409 XD 174L-3 KJ129410 Luan 116.255 31.360 174F-1 KJ129392 Wang 20120903 Lucilia cuprina 174F-2 KJ129393 XD 174F-3 KJ129394 Fuzhou Fujian 119.315 26.053 174J-1 KJ129402 Wei XY 20120927 174J-2 KJ129403 174J-3 KJ129404 174H-2 KJ129398 Wuyishan 118.023 27.734 174I-1 KJ129399 Wei XY 20120922 174I-2 KJ129400 174I-3 KJ129401 Shantou Guangdong 116.712 23.403 174B-1 KJ129386 Yue QY 20120518 174B-2 KJ129387 174B-3 KJ129388 Wuzhishan Hainan 109.671 18.880 174M-1 KJ129411 Wang 20121123 174M-2 KJ129412 XD 174M-3 KJ129413 Sanya 109.508 18.256 174N-1 KJ129414 Wang 20130507 174N-2 KJ129415 XD 174N-3 KJ129416 Zhengzhou Henan 113.685 34.762 174P-1 KJ129418 Qiu XY 20130812 174P-2 KJ129419 Xiaogan Hubei 114.120 31.556 174K-1 KJ129405 Wei XY 20121001 174K-2 KJ129406 174K-3 KJ129407 Nanjing Jiangsu 118.746 32.087 174E-1 KJ129389 Liu DX 20120829

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174E-2 KJ129390 174E-3 KJ129391 Jinan Shandong 117.025 36.675 174G-1 KJ129395 Wang 20120823 174G-2 KJ129396 XD Panzhihua Sichuan 101.636 26.711 174O-1 KJ129417 Liu DX 20130716 Jixian Tianjin 117.274 40.106 174A-1 KJ129383 Wang 20120816 174A-2 KJ129384 XD 174A-3 KJ129385 Hohhot Inner 111.680 40.707 236F-1 KJ129548 Hu J 20130804 Mongolia 236F-2 KJ129549 236F-3 KJ129550 Jinan Shandong 117.025 36.675 236D-1 KJ129545 Wang 20120823 Lucilia illustris 236D-2 KJ129546 XD 236D-3 KJ129547 Datong Shanxi 113.294 39.581 236G-1 KJ129551 Yue QY 20130810 236G-2 KJ129552 Jixian Tianjin 117.488 40.022 236A-1 KJ129543 Wang 20120816 236A-2 KJ129544 XD Chaohu Anhui 117.673 31.431 56N-1 KJ129320 Wang 20121002 XD Luan 116.255 31.360 56G-1 KJ129302 Wang 20120903 56G-2 KJ129303 XD

Lucilia sericata Chongqing Chongqing 106.429 29.821 56S-1 KJ129333 Liu DX 20130727 56S-2 KJ129334 56S-3 KJ129335 Fuzhou Fujian 119.315 26.053 56L-1 KJ129314 Wei XY 20120927 56L-2 KJ129315 56L-3 KJ129316 Wuyishan 118.023Draft 27.734 56H-1 KJ129305 Wang 20120924 56H-2 KJ129306 XD 56H-3 KJ129307 Shantou Guangdong 116.712 23.403 56D-1 KJ129293 Yue QY 20120518 56D-2 KJ129294 56D-3 KJ129295 Zhongshan 113.423 22.517 56A-1 KJ129292 Feng 20110318 XM Sanya Hainan 109.508 18.256 56O-1 KJ129321 Wang 20130426 56O-2 KJ129322 XD 56O-3 KJ129323 Shijiazhuang Hebei 114.411 38.070 56E-1 KJ129296 Wang 20120822 56E-2 KJ129298 XD Qinhuangdao 119.485 39.835 56J-1 KJ129308 Wang 20120918 56J-2 KJ129309 XD 56J-3 KJ129310 Xiangyang Hubei 112.178 32.045 56Y-1 KJ129339 Chen J 20130819 56Y-2 KJ129340 56Y-3 KJ129341 Xiaogan 114.117 31.558 56M-1 KJ129317 Wei XY 20120930 56M-2 KJ129318 56M-3 KJ129319 Jiaozuo Henan 113.386 35.421 56W-1 KJ129336 Hu J 20130813 56W-2 KJ129337 56W-3 KJ129338 Nanjing Jiangsu 118.880 31.322 56F-1 KJ129299 Liu DX 20120829 56F-2 KJ129300 56F-3 KJ129301 Jinan Shandong 117.025 36.675 56Aa-1 KJ129311 Wang 20120823 56Aa-2 KJ129312 XD Panzhihua Sichuan 101.636 26.711 56R-1 KJ129330 Wu KL 20130716 56R-2 KJ129331 56R-3 KJ129332 Lhasa Tibet 91.087 29.656 56P-1 KJ129324 Liu DX 20130628 56P-2 KJ129325 56P-3 KJ129326 Paizhen, 93.075 29.046 56Q-1 KJ129327 Liu DX 20130704 Nyingchi 56Q-2 KJ129328

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56Q-3 KJ129329 Luan Anhui 116.333 31.393 144G-1 KJ129356 Wang 20120903 144G-2 KJ129357 XD 144G-3 KJ129358 Wuyishan Fujian 118.023 27.734 83C-1 KJ129359 Wang 20120924 83C-2 KJ129360 XD 83C-3 KJ129361

Hemipyrellia ligurriens Chongqing Chongqing 106.429 29.821 144R-1 KJ129377 Liu DX 20130725 144R-2 KJ129378 144R-3 KJ129379 Shantou Guangdong 116.730 23.366 144C-1 KJ129344 Yue QY 20120517 144C-2 KJ129345 144C-3 KJ129346 Shaoguan 113.587 24.864 144S-1 KJ129380 J. L. 20130928 144S-2 KJ129381 Liao 144S-3 KJ129382 Yunfu 112.059 22.912 144D-1 KJ129347 Hu J 20120907 144D-2 KJ129348 144D-3 KJ129349 Zhanjiang 109.847 20.555 144A-1 KJ129342 Yue QY 20120509 144A-2 KJ129343 Wuzhishan Hainan 109.671 18.880 144O-1 KJ129362 Wang 20121123 144O-2 KJ129363 XD 144O-3 KJ129364 144O-4 KJ129368 144O-5 KJ129369 144O-6 KJ129370 Sanya 109.508 18.256 144N-1 KJ129365 Wang 20121120 Draft144N-2 KJ129366 XD 144N-3 KJ129367 Nanjing Jiangsu 118.880 31.322 144F-1 KJ129353 Liu DX 20120829 144F-2 KJ129354 144F-3 KJ129355 Taian Shandong 117.093 36.311 144E-1 KJ129350 Wang 20120826 144E-2 KJ129351 XD 144E-3 KJ129352 Emeishan Sichuan 103.493 29.591 144Q-1 KJ129374 Wu KL 20130722 144Q-2 KJ129375 144Q-3 KJ129376 Nyingchi Tibet 95.333 29.325 144P-1 KJ129371 Wu KL 20130709 144P-2 KJ129372 144P-3 KJ129373 Chaohu Anhui 117.872 31.642 150M-1 KJ129454 Wang 20121002 150M-2 KJ129455 XD Luan 116.255 31.360 150H-1 KJ129439 Wang 20120903 150H-2 KJ129440 XD

Musca domestica 150H-3 KJ129441 Fuzhou Fujian 119.315 26.053 150J-1 KJ129445 Wei XY 20120927 150J-2 KJ129446 150J-3 KJ129447 Xiamen 118.110 24.465 150I-1 KJ129442 Wei XY 20120917 150I-2 KJ129443 150I-3 KJ129444 Meizhou Guangdong 116.182 23.740 150L-1 KJ129451 Qiu DY 20121002 150L-2 KJ129452 150L-3 KJ129453 Shantou 116.712 23.403 150B-1 KJ129423 Yue QY 20120518 150B-2 KJ129424 150B-3 KJ129425 Yunfu 112.059 22.912 150N-1 KJ129456 Yue QY 20121020 Zhongshan 113.486 22.568 150A-1 KJ129420 Yang 20120809 150A-2 KJ129421 MF 150A-3 KJ129422 Fangchenggang Guangxi 108.055 21.892 150Q-1 KJ129462 Wang 20130407 150Q-2 KJ129463 XD 150Q-3 KJ129464

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Ledong Hainan 108.863 18.740 150R-1 KJ129465 Wang 20130428 150R-2 KJ129466 XD 150R-3 KJ129467 Haikou 110.316 20.034 150P-1 KJ129459 Wang 20121126 150P-2 KJ129460 XD 150P-3 KJ129461 Wuzhishan 109.523 18.789 150O-1 KJ129457 Wang 20121122 150O-2 KJ129458 XD Shijiazhuang Hebei 114.411 38.070 150E-1 KJ129432 Wang 20120823 150E-2 KJ129433 XD Qinhuangdao 119.485 39.835 150D-1 KJ129429 Wang 20120818 150D-2 KJ129431 XD Xiaogan Hubei 114.120 31.556 150K-1 KJ129448 Wei XY 20120930 150K-2 KJ129449 150K-3 KJ129450 Nanjing Jiangsu 118.746 32.087 150G-1 KJ129436 Wang 20120831 150G-2 KJ129437 XD 150G-3 KJ129438 Taian Shandong 117.093 36.311 150F-1 KJ129434 Wang 20120826 150F-2 KJ129435 XD Jixian Tianjin 117.274 40.106 150C-1 KJ129426 Liu DX 20120815 150C-2 KJ129427 150C-3 KJ129428 ------California, ------150S-1 KJ129468 Yue QY 20120926 USA 150S-2 KJ129469 150S-3 KJ129470 Luan Anhui 116.333 31.393 98F-1 KJ129485 Wang 20120903 98F-2 KJ129486 XD Draft98F-3 KJ129487 Fuzhou Fujian 119.315 26.053 98I-1 KJ129494 Wei XY 20120927 Musca Musca sorbens 98I-2 KJ129495 Xiamen 118.197 24.441 98G-1 KJ129488 Wei XY 20120919 98G-2 KJ129489 98G-3 KJ129490 Wuyishan 118.023 27.734 98H-1 KJ129491 Wei XY 20120925 98H-2 KJ129492 98H-3 KJ129493 Shantou Guangdong 116.717 23.372 98B-1 KJ129474 Yue QY 20120517 98B-2 KJ129475 Yunfu 112.059 22.912 98D-1 KJ129479 Yue QY 20121020 98D-2 KJ129480 98D-3 KJ129481 Zhongshan 113.333 22.303 98A-1 KJ129471 Huang 20111202 98A-2 KJ129472 YW 98A-3 KJ129473 Haikou Hainan 110.316 20.034 98K-1 KJ129496 Wang 20121126 98K-2 KJ129497 XD Shijiazhuang Hebei 114.411 38.070 98C-1 KJ129476 Wang 20120823 98C-2 KJ129477 XD 98C-3 KJ129478 Xiangyang Hubei 112.159 32.076 98M-1 KJ129501 Hu J 20130820 98M-2 KJ129502 98M-3 KJ129503 Yueyang Hunan 113.094 29.381 98N-1 KJ129504 Chen J 20130821 98N-2 KJ129505 98N-3 KJ129506 Nanjing Jiangsu 118.746 32.087 98Q-1 KJ129482 Wang 20120831 98Q-2 KJ129483 XD 98Q-3 KJ129484 Chaohu Anhui 117.673 31.431 163G-1 KJ129570 Wang 20121002 163G-2 KJ129571 XD 163G-3 KJ129572 Huoshan 116.333 31.393 163F-1 KJ129567 Liu DX 20120903 163F-2 KJ129568

Graphomya rufitibia 163F-3 KJ129569 Fuzhou Fujian 119.290 26.093 163L-1 KJ129585 Wei XY 20120926

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163L-2 KJ129586 163L-3 KJ129587 Wuyishan 117.986 27.625 163M-1 KJ129588 Wei XY 20120925 163M-2 KJ129589 163M-3 KJ129590 Meizhou Guangdong 116.182 23.740 163K-1 KJ129583 Qiu DY 20121002 163K-2 KJ129584 Shantou 116.717 23.372 163Aa-1 KJ129553 Yue QY 20120517 163Aa -2 KJ129554 163Aa -3 KJ129555 Yunfu 112.059 22.912 163E-1 KJ129564 Yue QY 20121020 163E -2 KJ129565 163E -3 KJ129566 Fangchenggang Guangxi 108.055 21.892 163I-1 KJ129576 Wang 20130407 163I -2 KJ129577 XD 163I-3 KJ129578 Guilin 110.253 25.915 163J-1 KJ129579 Wang 20130411 163J -2 KJ129580 XD 163J -3 KJ129581 Sanya Hainan 109.508 18.256 163H-1 KJ129573 Wang 20121120 163H-2 KJ129574 XD 163H-3 KJ129575 Yulongtan, Shandong 117.025 36.675 163Ba-1 KJ129562 Wang 20120823 Jinan 163Ba-2 KJ129563 XD Daminghu, 117.015 36.666 163C-1 KJ129556 Wang 20120825 Jinan 163C-2 KJ129557 XD 163C-3 KJ129558 163C-4 KJ129559 Draft163C-5 KJ129560 163C-6 KJ129561 Weinan Shannxi 109.430 34.517 163O-1 KJ129591 Hu J 20130815 163O-2 KJ129592 163O-1 KJ129593 Luan Anhui 116.255 31.360 90B-1 KJ129151 Wang 20120903 90B-2 KJ129152 XD 90B-3 KJ129153 Fuzhou Fujian 119.290 26.093 90K-1 KJ129167 Wei XY 20120926 90K-2 KJ129168

Sarcophaga albiceps Wuyishan 118.023 27.734 90L-1 KJ129169 Liu DX 20120924 Meizhou Guangdong 116.182 23.740 90Q-1 KJ129179 Yue QY 20121002 Shantou 116.712 23.403 90H-1 KJ129164 Yue QY 20120518 Shenzhen 114.550 22.533 90I-1 KJ129165 Wang 20120516 90I-2 KJ129166 XD Yunfu 112.059 22.912 90G-1 KJ129161 Yue QY 20121020 90G-2 KJ129162 90G-3 KJ129163 Zhanjiang 109.847 20.555 90F-1 KJ129159 Wang 20120508 90F-2 KJ129160 XD 90F-3 KJ129147 Huang 20111109 YW Wuzhishan Hainan 109.671 18.880 90N-1 KJ129170 Wang 20121123 90N-2 KJ129171 XD 90N-3 KJ129172 90N-4 KJ129173 Qinhuangdao Hebei 119.485 39.835 90A-1 KJ129148 Wang 20120918 90A-2 KJ129149 XD 90A-3 KJ129150 Nanjing Jiangsu 118.880 31.322 90E-1 KJ129157 Liu DX 20120829 90E-2 KJ129158 Taian Shandong 117.093 36.311 90O-1 KJ129174 Wang 20120826 90O-2 KJ129175 XD Jinan 117.025 36.675 90P-1 KJ129176 Wang 20120823 90P-2 KJ129177 XD 90P-3 KJ129178 Jixian Tianjin 117.488 40.022 90D-1 KJ129154 Wang 20120816 90D-2 KJ129155 XD

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90D-3 KJ129156 Luan Anhui 116.333 31.393 208K-1 KJ129195 Wang 20120903 208K-2 KJ129196 XD 208K-3 KJ129197 Fuzhou Fujian 119.290 26.093 208N-1 KJ129200 Wei XY 20120926 208N-2 KJ129201 208N-3 KJ129202

Sarcophaga brevicornis Wuyishan 118.023 27.734 208M-1 KJ129198 Wang 20120924 208M-2 KJ129199 XD Meizhou Guangdong 116.141 24.316 208E-1 KJ129184 Yue QY 20121004 208E-2 KJ129185 208E-3 KJ129186 Shenzhen 114.213 22.585 208C-1 KJ129182 Wang 20120514 208C-2 KJ129183 XD Zhongshan 113.423 22.517 208A-1 KJ129180 Wei XY 20130222 208A-2 KJ129181 Haikou Hainan 110.317 20.015 208Q-1 KJ129209 Wang 20121126 208Q-2 KJ129210 XD 208Q-3 KJ129211 Sanya 109.508 18.256 208O-1 KJ129203 Wang 20121120 208O-2 KJ129204 XD 208O-3 KJ129205 Wuzhishan 109.523 18.789 208P-1 KJ129206 Wang 20121122 208P-2 KJ129207 XD 208P-3 KJ129208 Shijiazhuang Hebei 114.353 37.909 208G-1 KJ129187 Wang 20120822 XD Nanjing Jiangsu 118.880 31.322 208J-1 KJ129192 Liu DX 20120829 Draft208J-2 KJ129193 208J-3 KJ129194 Jinan Shandong 117.025 36.675 208I-1 KJ129190 Wang 20120823 208I-2 KJ129191 XD Taian 117.093 36.311 208H-1 KJ129188 Wang 20120826 208H-2 KJ129189 XD Fuzhou Fujian 119.290 26.093 69M-1 KJ129239 Wei XY 20120926 69M-2 KJ129240 69M-3 KJ129241 Xiamen 118.090 24.458 69K-1 KJ129233 Wang 20120903 69K-2 KJ129234 XD 69K-3 KJ129235 Sarcophaga peregrina Wuyishan 118.023 27.734 69L-1 KJ129236 Wang 20120924 69L-2 KJ129237 XD 69L-3 KJ129238 Yunfu Guangdong 112.059 22.912 69C-1 KJ129216 Yue QY 20120907 69C-2 KJ129217 69C-3 KJ129218 Zhanjiang 109.847 20.555 69B-1 KJ129213 Yue QY 20120509 69B-2 KJ129214 69B-3 KJ129215 Zhongshan 113.423 22.517 69A-1 KJ129212 Wei XY 20130222 Shijiazhuang Hebei 114.353 37.909 69E-1 KJ129222 Wang 20120822 69E-2 KJ129223 XD 69E-3 KJ129224 Xiaogan Hubei 114.120 31.556 69N-1 KJ129242 Wei XY 20120930 69N-2 KJ129243 Nanjing Jiangsu 118.880 31.322 69H-1 KJ129230 Liu DX 20120829 69H-2 KJ129231 69H-3 KJ129232 Jinan Shandong 117.025 36.675 69F-1 KJ129225 Wang 20120823 69F-2 KJ129226 XD 69F-3 KJ129227 Taian 117.093 36.311 69G-1 KJ129228 Wang 20120826 69G-2 KJ129229 XD Jixian Tianjin 117.488 40.022 69D-1 KJ129219 Wang 20120816 69D-2 KJ129220 XD 69D-3 KJ129221

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Figure S1 Maximum likelihood tree showing relationships between three calliphorid flies: Chrysomomya megacephala (blue), Chrysomya pinguis (green), and Protophormia terranovae (red), with Achoetandrus rufifaces assigned as the outgroup. The dataset included 53 unique C megacephala , 13 C. pinguis , and 5 P. terraenovae COI barcode region sequences. Numbers above branches are bootstrap support (1000 replicates) for neighbor-joining distance analysis using a maximum likelihood distance model. See methods for more details. Both the maximum likelihood and neighbor-joining bootstrap analysis strongly support three distinct clades, one for each species.

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